import tensorflow as tf
 
slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
 
 
def inception_v3_base(inputs, scope=None):
    end_points = {}
 
    with tf.variable_scope(scope, 'InceptionV3', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='VALID'):
            # 299 x 299 x 3
            net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')
            # 149 x 149 x 32
            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
            # 147 x 147 x 32
            net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
            # 147 x 147 x 64
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
            # 73 x 73 x 64
            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
            # 73 x 73 x 80.
            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
            # 71 x 71 x 192.
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
            # 35 x 35 x 192.
 
        # Inception blocks
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                            stride=1, padding='SAME'):
            # mixed: 35 x 35 x 256.
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_1: 35 x 35 x 288.
            with tf.variable_scope('Mixed_5c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_1_0c_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_2: 35 x 35 x 288.
            with tf.variable_scope('Mixed_5d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_3: 17 x 17 x 768.
            with tf.variable_scope('Mixed_6a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 384, [3, 3], stride=2,
                                           padding='VALID', scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2,
                                           padding='VALID', scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
 
            # mixed4: 17 x 17 x 768.
            with tf.variable_scope('Mixed_6b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_5: 17 x 17 x 768.
            with tf.variable_scope('Mixed_6c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # mixed_6: 17 x 17 x 768.
            with tf.variable_scope('Mixed_6d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_7: 17 x 17 x 768.
            with tf.variable_scope('Mixed_6e'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            end_points['Mixed_6e'] = net
 
            # mixed_8: 8 x 8 x 1280.
            with tf.variable_scope('Mixed_7a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2,
                                           padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                    branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2,
                                           padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                               scope='MaxPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
            # mixed_9: 8 x 8 x 2048.
            with tf.variable_scope('Mixed_7b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(
                        branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(
                        branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
 
            # mixed_10: 8 x 8 x 2048.
            with tf.variable_scope('Mixed_7c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([
                        slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                        slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(
                        branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = tf.concat([
                        slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                        slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(
                        branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            return net, end_points
 
 
def inception_v3(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='InceptionV3'):
    with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes],
                           reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout],
                            is_training=is_training):
            net, end_points = inception_v3_base(inputs, scope=scope)
 
            # Auxiliary Head logits
            with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                                stride=1, padding='SAME'):
                aux_logits = end_points['Mixed_6e']
                with tf.variable_scope('AuxLogits'):
                    aux_logits = slim.avg_pool2d(
                        aux_logits, [5, 5], stride=3, padding='VALID',
                        scope='AvgPool_1a_5x5')
                    aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
                                             scope='Conv2d_1b_1x1')
 
                    # Shape of feature map before the final layer.
                    aux_logits = slim.conv2d(
                        aux_logits, 768, [5, 5],
                        weights_initializer=trunc_normal(0.01),
                        padding='VALID', scope='Conv2d_2a_5x5')
                    aux_logits = slim.conv2d(
                        aux_logits, num_classes, [1, 1], activation_fn=None,
                        normalizer_fn=None, weights_initializer=trunc_normal(0.001),
                        scope='Conv2d_2b_1x1')
                    if spatial_squeeze:
                        aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
                    end_points['AuxLogits'] = aux_logits
 
            # Final pooling and prediction
            with tf.variable_scope('Logits'):
                net = slim.avg_pool2d(net, [8, 8], padding='VALID',
                                      scope='AvgPool_1a_8x8')
                # 1 x 1 x 2048
                net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
                end_points['PreLogits'] = net
                # 2048
                logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                                     normalizer_fn=None, scope='Conv2d_1c_1x1')
                if spatial_squeeze:
                    logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
                # 1000
            end_points['Logits'] = logits
            end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
    return logits, end_points
 
 
def inception_v3_arg_scope(weight_decay=0.00004,
                           stddev=0.1,
                           batch_norm_var_collection='moving_vars'):
    batch_norm_params = {
        'decay': 0.9997,
        'epsilon': 0.001,
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
        'variables_collections': {
            'beta': None,
            'gamma': None,
            'moving_mean': [batch_norm_var_collection],
            'moving_variance': [batch_norm_var_collection],
        }
    }
 
    with slim.arg_scope([slim.conv2d, slim.fully_connected],
                        weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope(
                [slim.conv2d],
                weights_initializer=trunc_normal(stddev),
                activation_fn=tf.nn.relu,
                normalizer_fn=slim.batch_norm,
                normalizer_params=batch_norm_params) as sc:
            return sc
 
 
from datetime import datetime
import math
import time
 
 
def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    total_duration_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d, duration = %.3f' %
                      (datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            total_duration_squared += duration * duration
    mn = total_duration / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
          (datetime.now(), info_string, num_batches, mn, sd))
 
 
if __name__ == '__main__':
    batch_size = 32
    height, width = 299, 299
    inputs = tf.random_uniform((batch_size, height, width, 3))
    with slim.arg_scope(inception_v3_arg_scope()):
        logits, end_points = inception_v3(inputs, is_training=False)
 
    init = tf.global_variables_initializer()
    sess = tf.Session()
    sess.run(init)
    num_batches = 100
    time_tensorflow_run(sess, logits, "Forward")
